opportunities in operations researchpandit/orweekend/brenda_or_opportunities.pdf · -real-time...

© Copyright IBM Corporation 2005

Opportunities in Operations Research

Brenda DietrichApril 8, 2006

2 © Copyright IBM Corporation 2005

Agenda

Personal Historical PerspectiveTechnology Trends OR Opportunities: Internal ViewOR Opportunities: External ViewInterconnections to other IT trendsPriorities Going Forward


Personal Historical Perspective

1980’s – Manufacturing Logistics-OR models for portions of IBM manufacturing lines

Focus on layout of line

- “micro logistics” – models of individual automated machines-Scheduling of process or “sector”

1990’s – Optimization Center-Enterprise level planning -Supply chain models and tools-OSL-Client OR applications-First applications in IBM services

2000’s – Mathematical Sciences- “Analytic” applications throughout IBM-Client engagements with IBM Consultants-Open Source Software


Trends

Framework for thinking about trends

Technology Trends-Processing Speed -Data Availability-Communication-Software and Application Architecture

Business Trends

The Event Driven World


The Technology Curve of Change

•The first technological steps--sharp edges, fire, the wheel--took tens of thousands of years.

•By 1000 A.D. a paradigm shift required only a century or two.

•In the nineteenth century, there was more technological change than in the nine centuries preceding it

•In the first twenty years of the twentieth century, we saw more advancement than in all of the nineteenth century.

•Today paradigm shifts occur in only a few years time. The World Wide Web did not exist in anything like its present form just a few years ago; it didn't exist at all a decade ago.

Source: The Law of Accelerating Returns –Ray Kurtzweilhttp://www.kurzweilai.net/articles/art0134.html?printable=1


The Curve of Change

World Population

0

2

4

6

8

10

12

1500

1550

1600

1650

1700

1750

1800

1850

1900

1950

2000

2050

Bill

ions

Low Medium High

Source: Population Division of the Department of Economic and Social Affairs of the United Nations http://esa.un.org/unpp

Source: The Gary Hilbert Letterhttp://www.thegaryhalbertletter.com/newsletters/population.htm

1. There are more people alive today... than all the humans who have ever lived since the dawn of civilization.

2. 99% of all the scientists who have ever lived... are alive today.

3. More info is contained in one daily edition of the New York Times… than wasavailable inthe entire 17th century.


Technology Deployment Pace Accelerates (US data)

0

10

20

30

40

50

60

70

80

90

100

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

Hou

shol

ds (%

)

WWW

Credit Card

A

Business & Technology Infrastructure

Customer Segments

Insi

ght

Ris

k &

Fin

anci

alM

anag

emen

t

Processing

Distribution

Manufacturing

IBM Research

© 2005 IBM Corporation 8

Processing Speed: What $1000 Buys

1900 1920 1940 1960 1980 2000 20201E-6

1E-3

1E+0

1E+3

1E+6

1E+9

1E+12MechanicalElectro-mechanical

Vacuum tubeDiscrete transistor

Integrated circuit

Year

1,000,000,000,000

1,000,000,000

1,000,000

1,000

1

0.001

0.000001

Com

puta

tions

/ se

c

after Kurzweil, 1999 & Moravec, 1998


Data and Storage Trends

Storage Density

0.001

0.01

0.1

1

10

100

1000

1980 1990 2000 2010

Are

al D

ensi

ty (G

b/in

2)

Drives CD Blue LaserTape Lab Demos Millipede

Storage Price

0.0001

0.001

0.01

0.1

1

10

100

1000

1980 1990 2000 2010

Pric

e / M

egaB

yte

($)

HDD DRAM Flash Paper/Film

Price decreasing rapidly, now significantly cheaper than paper.

Progress could slow down due to technological challenges

Progress from breakthroughs, including MR, GMR heads, AFC media

Range of Paper/Film

Since 1997 raw storage prices have been declining at 50%-60% per year

1" Micro drive

2.5" HDD

3.5" HDD

Flash

DRAM

CD-ROM

DVD-R

i


Data Volume is Exploding

Machine Generated Data-Sensors/Biometrics/Voice-High Volume-Not amenable to

traditional database architectures

Authored Data-Created by hand-Historically in database-Low volume (but high

value)

Machine-generated versus authored data

.001

.01

.1

1

10

100

1,000

1995 2000 2005 2010 2015

Storage online

In databases

All medical imaging

Personal multimedia

Surveillance data

Text data

Static Web data

Gig

a-by

tes

/ US

cap

ita /

year

Machine generated data is increasing at an exponential pace which causesusability concerns


Data Being Captured at Increasing Spatiotemporal Resolution

People sensors:location (inferred from that of their devices), activities (inferred from calendar and desktop information), biometrics, etc.

Place sensors:room status (inferred from anonymous motion/sound detectors), presence (of people and things), congestion (inferred from pressure pads or camera images), etc.

Thing sensors:location (RFID), status (monitoring sensors such as telematics, desktop), etc.

Business sensors:context from databases (medical data, credit history, location history), context from processes


Connectivity: Internet Access Trends

Note: Subscriptions - not users !

Global Internet Subscriptions

0

200

400

600

800

1000

2001 2002 2003 2004 2005 2006 2007

Mill

ions

Asia/Pacific North AmericaWestern Europe Middle East & AfricaLatin America Eastern Europe

Source: Gartner Dataquest (June 2003)

Broadband Connectivity

Source: IDC, May 2003

US29% AP

39%

W.Eur25%

row7%

Worldwide Broadband Connections Trend

0

100

200

300

2003 2004 2005 2006 2007

Mill

ions

► Growth driven by rapid decline in price of service ► Largest growth in Asia Pacific ► DSL/cable modems dominate “Last Mile”

2003 Broadband Connectivity by Geography

Wireless

0

50

100

150

2002 2003 2004 2005 2006 2007

(Units in Thousands)

Source: In-Stat/MDR April 2003

Worldwide Hotspot Locations► 150,000 public WLAN

hotspots by 2007► 35M WLAN access units

by 2005


Communication trends

-Trend Doubling Period

-Communications- bits/dollar before 1995 79 months-Communications- bits/dollar with DWDM 12 months

-Maximum Internet Trunk Speed in service 22 months

- Internet Traffic Growth 1969-1982 21 months - Internet Traffic Growth 1983-1997 9 months - Internet Traffic Growth 1997-2008 6 months

- Internet Router/Switch Max Speed until 1997 22 months - Internet Router/Switch Max Speed after 1997 6 months

http://www.packet.cc/files/InternetTrends.htm


Web 2.0 Meme (Web Culture)

The Web as

“The Platform”

Tools: RSS, AJAX, PHP,

Ruby

Services, not packaged software

Architectural participation

Small pieces loosely joined, or

“re-mixed”

Harnessing collective

intelligence

Software that gets better as more people use it

Standards: REST, XHTML

Techniques: Mash-up, wiki,

tagging, blogging

Rich user experiences

Light-weight programming

models

Zeitgeist of the current internet generation


Making Web Services as Simple as Spreadsheets

1. Locate a web service providing stock quotes in dollars

2. Reuse an existing web service for currency exchange

3. Goal: new aggregate web service, Stock Quote in any currency

Radically simplified creation, publishing, deployment, and reuse of web services

Result: Integration by end-users in minutes

click to run

4. Solution: Wire A and B together, using a spreadsheet metaphor, to create and publish a new web service


The Event-Driven World

There is a growing need to monitor, capture, process, and store massive volumes of time-dependent events- Smart sensors, RFID, program trading, fraud management, risk and compliance, intelligent oil

field, location based service, logistics, presence (SIP), in-line analytics, etc.

An increasing number of companies are addressing the needs of the time-dependent infrastructure market- Developing event engines that route, transform and derive events from multiple streams- Delivering content management solutions supporting large volumes of time-dependent data- Extracting event information from applications (ERP, CRM, etc.) and sensors

Standards are emerging- Real-time Specification for Java (JSR 1), Distributed Real-Time Specification (JSR 50)- Web Services Notification and Web Services Eventing- OMG Data Distribution Service (DDS) and IEEE 1516 for battlefield simulation

Middleware will evolve to deal with the throughput and time-dependent needs of the event-driven world. New programming models and tools will also emerge- Data integration will evolve to event integration

The accelerating need to handle large volumes of time-dependent events will give rise to new classes of middleware, programming models, and tools


The Prerequisites for effective use of Operations Research are in place or being put in place

Computational AdvancesExponential increases in

processing power

Advanced Algorithmsand New Models

OptimizationSimulation

Data mining

Data AvailabilityData is abundant, including

real-time data

Success of OR deployments will depend upon fit, value, and ease of use


$10M savings 30% productivity from tools that teach

Ford’s Auto Plant SimulationHR Management

$890M reduction in finished goods inventory; improved on time shipments 63% to 92%

John Deere’s Inventory and Asset Management Supply Chain

$200M savings; optimal forecasting and allocation

U.S. Army RecruitingHR Management

$200M cost savings from optimal sourcingMotorola’s Supplier Negotiation ProcessesProcurement

$750M savings in inventoryIBM’s Asset ManagementSupply Chain

$80M revenue growth new offerings increase customers choices

Merrill Lynch ‘s Pricing Analysis ProcessesFinancial Services

$150M per year cost savingsBritish Telephone’s Field Engineer SchedulingWorkforce Scheduling

Reduced staff 10%; increased utilization 20% maintained SLA>90%

Bombardier’s Aircraft Operations and Crew Scheduling Processes

Workforce Scheduling

MeasuresCompanyProcess

Examples of Savings from Operations ResearchEdelman Prize

Institute for Operations Research and Management Sciences (INFORMS)


Examples of Savings from Operations Research (continued)

$44M savings; reduced number of trucks, improved customer service levels

Waste Management’s Route Optimization

Transportation

$5M savings from optimal portfolio of more than 150 negotiated contracts

Texas Children’s Hospital’s Contract Negotiations

Healthcare

$30M direct savings, 50% more capacity, $300M cost avoidance to build more

Hong Kong Container Terminal Container Movements

Transportation

$170M savings optimal schedules reduced number of trains, shipment times

Canadian Pacific Railway’s Train Schedules

Transportation

Moved from #6 to #2 in its market by optimal mailings

Mail Order Firm’s Data-driven MarketingMarketing and Advertising

$50M revenue per year from optimal capacity (ad air-time) to meet customer demand

NBC’s Advertising SalesMarketing and Advertising


OR Opportunities: Internal view

Hybrid approaches, including techniques from AI as well as OR-AI based search strategies with MP based fathoming-Mathematical Optimization implementations of support vector machines

Non linear Optimization with some discrete variables

Effective exploitation of massive parallelism-1000’s of nodes

Robust Optimization

OR applications as a web service Open Source www.coin-or.org


Hybrid Approaches

Hybridization of traditional OR methods with CP (mainly) and other AI formalisms-CP based search strategies in B&B-LP based fathoming in CP search- “all different” constraint -GRASP for solution completion at B&B nodes-Highly successful new conference devoted to this: CP-AI-OR (workshop since 2000,

full int'l conference since 2004)-A large percentage of papers dealing with hybridized CP/LP models and algorithms

in CP'05 (a significant jump compared to CP'04).

Extensions to other “search” approaches?-How to use LP/IP methods be used within a neighborhood search framework to

identify attractive directions?-How to use LP/IP within a population based method to identify attractive

recombination or mutation opportunities?Or will intelligent recombination defeat the benefits of randomization?


Optimization methods in Data Mining and Machine Learning

Math programming formulations of the separation problemActive set approach to dealing with large data setsProvable algorithm performance

-Workshop on "Machine Learning, SVM and Large Scale Optimization", Turnau, Germany 2005.

-Workshop on ”Mathematical Programming in Data Mining and Machine Learning,”McMaster University 2005,

-EURO “Summer Institute on Optimization in Data Mining” was held in Ankara in July of 2004.

- “Workshop on Mathematical Programming in Data Mining and Machine Learning” will be held in January 2007


Non-linear Discrete Optimization

Mixed Integer Non Linear Programming (MINLP)- Solver development: some work underway in COIN-OR - Formulation issues: expect that as with LP/IP early solvers will be sensitive to problem formulation

Applications: Process industry, Finance Industry- Business goal is finding feasible, good solutions quickly.- Dynamic environment, imprecise data, approximate formulation limit value of optimizing

Need new algorithmic paradigm aimed at quickly finding good feasible solutions

Experimentation- Problem testbed that allows for experimentation with formulation and noise in the data- Diversity in domain, structure, and size.-

Fundamental structural results and complexity issues - Identification of special sub-problems

Challenges: - Finding faster methods for SDP (and in particular second-order cone programming) to take advantage of the tight

bounds in MINLP

Formulations that lead to good feasibility heuristics


Massive Parallelization

High performance machines like BlueGene have 1000’s of compute nodes-2 chips, each with Dual 700mHz processors with 4MB L3 Cache, -Up to 1GB memory

Open source SW allows a solver on every compute nodeWhich classes of problems are most suited to this environment?-Clearly the “embarassingly” parallel, but what else?

Which classes of solution algorithms are most suited to this environment?-With Branch and Bound philosophy, problem list grows slowly-Tradeoff between computation and inter-node communication

How can/should large problems be partitioned?-Reformulations that facilitate partitioning

What new problem spaces can be addressed with such massive computing power?


Robust Optimization

Community is still grappling with defining the right modeling paradigm.

One class of robust Optimization deals with resource planning with an infinite or rolling horizon.- Have nearly complete data for the first few periods, - At best partial data for the later periods.

Can forecast “average” data for later periods (e.g;, number of rides, average duration) And have historical data that could be sampled

Typical OR approach- Fix the planning horizon “far enough”- Use actual data for first few periods, estimates for later periods- Resolve at least at the end of each period

Issues: - Is the first period solution close to the true stochastic infinite-horizon solution- How much does the quality of the estimate for later periods matter?

Can we borrow ideas from statistical physics and "probabilistic reconstruction“- Unknown future is providing some sort of randomized boundary condition. - In some cases the local (now) solution is essentially independent of the boundary condition. - Expect a phase transition in some parameter such as the fraction of demand that arises unpredictably, so that

tightly coupled systems that are predictable far into the future will behave very differently from loosely coupled ones.


OR Opportunities: External view

Models and tools for planning and managing business to business services -Supply chain analogies - but without inventory as a lever

Models of human behavior-Workforce, customers

Event-based Decision Support -E.g. customized pricing, traffic conditions based routing,

Planning under uncertainty-Stochastic Optimization-Risk-based as well as expected value based

Business eco-system dynamics -Models for setting strategies and determining “next move”


Business to Business Services

Environment characterized by -Relatively small number of large, long duration deals-Relatively few providers for any service-Within a deal multiple services, delivered as requested over time and geography-Complex deal prices, based on volume, service level and other factors-Shared resources serving multiple clients-On going automation and other cost take-out initiatives

Issues-Forecasting of deals (opportunities and/or signings)-Translating demand (deals) into requirements (resources) -Pricing deals (or supporting price negotiations)-Planning/acquiring capacity -Allocating scare resources-Estimating investment ROI


Models of Human Behavior

In Service-based economies, and especially in industries based on human knowledge, the key resource to be “managed” is the individual human.-Supply chain models, based on large volumes of interchangeable parts, have limited

applicability-Skill taxonomies provide some useful data for short-term planning, but capture only

current skill, not attainable skills of relative distance between skillsTeam dynamics and multi-tasking lead to non-linear production functions-Data to quantify nonlinearities is not readily available.

There are few mature models of incentives, retention, workload-based performance, learning, etc.-And major cultural and generational differences exist.

Market-based models (individuals bidding for work assignments) have been proposed-Winner selection problem must capture complex constraints

Agent-based approaches have been proposed-But need good agent representations


Social Network Analysis (SNA) Tool Example

Source: Rob Cross, McIntyre School of Commerce, University of Virginia, http://www.robcross.org/

Insights from SNA

- Organizational charts may not show how work really gets done

- Senior executives not always central

SNA can reveal problems:

- Central person could be overworked

- Risk to organization if central person goes away

- Peripheral people can represent untapped knowledge

Social network analysis reveals hidden connections providing an “organizational X-ray”


General Description of SNO

SNO (Social Network Optimization)-The application of mathematical algorithms to solve optimization problems related to information and communication networks of people

How does SNO relate to SNA (Social Network Analysis)?-SNA provides the input to SNO-SNO extends traditional SNA by considering additional graph theoretically based concepts (e.g. transmission dynamics. k-paths, reliable paths)-SNO extends the descriptive nature of SNA to prescriptive (optimization) decision support-SNA provides diagnosis to problems and improvement opportunities. SNO provides decision support for what to do next

How does SNO relate to Social Computing?-S.C. (e.g. wikis, blogs, email, IM, browser analyzers) provide a means of automating capture of SN data

From: Helander and Melachrinoudis (1997). Facility Location for Reliable Route Planning in Hazardous Material Transportation. Transportation Science, 31(3):216-226

SocialComputing

SNA

Data

SNOData

Surveys Data

EnableObserve, Describe, Diagnose

Optimize

OtherData


Transaction-time Decision Support

Goal: optimal responses to events:-Use of more complete, more accurate, and more flexible

models of business systems-Use of real-time integrated data and computational power-Enables the generation and communication of timely

actionable decisions-May be embedded in automated process-May serve as advisor to human in the loop

Real WorldModel World

Data Analysis

LocalInfo

Decision Support

Example: Fleet Optimization

Challenge Planning and Dispatching coaches and drivers under dynamic traffic conditions and client requirements

Solution A planning and dispatching system based on start-of the art optimization, linked to reservation system and real-time data feeds.

Benefits Increased utilization of fleet and drivers (20%), improved customer satisfaction, and increased revenue (10%).


Planning under uncertainty

There is an increasing need to captures and characterizes variability and uncertainty in the outside world:-Extend models to include both internal variability and

external volatility due to environmental effects-Use predictive models and filtering techniques based on

real time data from multiple sources-Enable robust decision making, risk/rewards trade-offs

and consideration of recourse


Stochastic Planning

StatisticalInfo

Data Analysis

LocalInfo

Event based Optimization

Evaluation of multiple demand patterns and pricing structures.Allows linkage to resource planning and deployment models

E-business on Demand PricingPrice

RevenueCost structure

Market structure

Costs

Profit

Demand

Multiplexing gain Capacity

Optimize with respect to price

Simulate with respect to demand


Modeling Eco-system Dynamics

Model a decision unit and the rest of the world as interacting entities:-Capture dynamic interactions within complex systems at

multiple timescales-Avoid negative effects of localized decision making, e.g.

Bullwhip Effect, Stock market fluctuations-Requires new methods for analysis, modeling and

optimization


Collectiveand Dynamic

InfoAdaptive Optimization

Stochastic Optimization

StatisticalInfo

Data Analysis

LocalInfoEvent based

Optimization Electric Power Scheduling and Trading

The uncertainties in demand and prices over time together with the fixed grid topology and limited adaptability in the control mechanisms can result in local prices to vary over a very wide range. Adaptive control mechanisms that incorporate system dynamics can eliminate instabilities

0

28,000

56,000

84,000

112,000

140,000

1 3 5 7 9 11 13 15 17 19 21 23

HoursM

WH

Dem

and

0

36

72

108

144

180

Pric

e / M

WH

Demand Locality 1 Locality 2

0

28000

56000

84000

112000

140000

1 3 5 7 9 11 13 15 17 19 21 23

Hours

MW

H D

eman

d

0

36

72

108

144

180

Pric

e / M

WH

Demand Locality 1 Locality 2


Adaptive Optimization

Signals ActionsEvaluate

Environment

Environment

The appropriate level for modeling and optimization depends upon one’s control points.

If you own the entire system, you can use a model of the entire system to determine actions which attain desired system behaviors.

If you only control one entity, then you can attempt to model the other players in the system, taking into account the likely effect of your own actions on them.

Evaluate

“Unit of control”


Links to Business Process Modeling

Emerging “science” around Business Process Modeling- model, design and transform Business Processes in a formal, structured way. - provide a structure that links a company's fundamental strategic mission with a hierachy of process models down

to an operational and I/T level. -

In some cases, leveraging new OR models requires that processes be transformed.- No data, no process to get the data, or no process for which decision makers can use the models

With knowledge of what OR models can do, business process modeling can drive from high level objectives and then to design of processes and and I/T flows that would enable a DSS/OR model.

The formalization of Process Modeling tools allows for OR to be embedded in the Process Models themselves

Business process modeling tools, for example, include simulation components to do performance testing of a Process.

Can add additional OR capability to allow things like risk assessment on top of the business process Can embed an operational OR model as a web service within a business process simulation to test and design various configurations of the process and the SCE model parameters.


Opportunities in Operations ResearchThere is a new opportunity to us Operations Research to provide value to industry- Model and analyze complex dynamics resulting from variability together with increasing speed, increasing

interdependence and shortening time-scales- Use these models and related analytic and optimization methods to support strategic, tactical and operational

business decisions- Collect, manage, and distribute the huge amounts of raw and analyzed data that describe the behavior of complex

business systems- Provide massive computational capability to businesses interested in exploiting advanced analytic and optimization

methods

In order to capitalize on this opportunity there must be close collaborations Operations Research and others in the Information technology industry- We must develop methods for both analyzing complex dynamics and computing unified optimal solutions across

multiple time scales and methods- We must provide methods for adaptive refinement of models and analysis methods as data, objectives or

environment changes- We must develop modeling and analysis techniques and algorithms that work efficiently on large-scale computing

infrastructures

IBM Research

Global Technology Outlook 2005 IBM Confidential / Do Not Distribute © 2004 IBM Corporation37

On Demand

Robust

Responsive

Controlled

Visible

Automated

Static Process Workflows

InformationRationalization

Automated Responses to events

Levels of Maturity

Dashboard Reporting

Transaction Processing

Workflows & Event Monitoring

Responsive Control

Impact to the Organization

As level of automation, information availability, and maturity increase, new uses for Operations Research emerge

Robust management

Adaptive business

Accommodate volatility/variability

Exploit environmental dynamics

opportunities in operations researchpandit/orweekend/brenda_or_opportunities.pdf · -real-time...

Documents